The presence of non-uniform lighting and/or shadows, which create variations in image intensity levels, may detrimentally affect the reliability of Optical Character Recognition (OCR) techniques applied to the images. In many OCR approaches, text is recognized in an image by localizing each word and/or character in the image, which is followed by classification of the word and/or character. Recent localization techniques have focused on pixel grouping/merging approaches because they yield good text localization results with relatively low computational overhead.
For example, Maximally Stable Extremal Regions (MSER) based methods are often used for character segmentation during OCR. MSER based methods typically identify regions of contiguous pixel sets (termed “Connected Components”), whose pixel intensities differ from pixel intensities within a region bounded by the Connected Component (CC) by some predetermined threshold, and where the size of the CC is stable across several intensity thresholds. MSERs may be viewed as CCs, where pixels inside the CC have either higher or lower intensity than all pixels on the CC.
Because MSER identification is dependent on pixel intensities, the presence of shadows or non-uniform lighting in images may impact the identification of CCs. The term “shadows” as used herein refers broadly to variations in pixel intensities caused by non-uniform lighting and/or contrast variations. For example, when shadows are present, only a portion of a word in an image may be obtained as a connected component, thereby leading to errors or inaccuracies in the OCR process.
Therefore, there is a need for systems, apparatus and methods that facilitate robust and accurate recovery of words and/or characters reliably in the presence of non-uniform lighting and/or shadows.